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Our deep neural network was trained on a large number of synthetic spectra, with complete control over the features represented in the samples. We show that our model can detect signal regions effectively and minimize classification errors between different types of resonance patterns. We demonstrate that the network generalizes remarkably well on real experimental <jats:sup>1<\/jats:sup>H NMR spectra.<\/jats:p>","DOI":"10.3389\/frai.2022.1116416","type":"journal-article","created":{"date-parts":[[2023,1,11]],"date-time":"2023-01-11T06:57:25Z","timestamp":1673420245000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":10,"title":["Automatic classification of signal regions in 1H Nuclear Magnetic Resonance spectra"],"prefix":"10.3389","volume":"5","author":[{"given":"Giulia","family":"Fischetti","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nicolas","family":"Schmid","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Simon","family":"Bruderer","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guido","family":"Caldarelli","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alessandro","family":"Scarso","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andreas","family":"Henrici","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dirk","family":"Wilhelm","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2023,1,11]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"30029","DOI":"10.1073\/pnas.2020596117","article-title":"The science of deep learning","volume":"117","author":"Baraniuk","year":"2020","journal-title":"Proc. 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